2smart cloud Python API Docs | dltHub

2Smart Cloud is an IoT platform that provides device management, SDKs, and an MQTT‑based communication protocol. The REST API base URL is https://cloud.2smart.com and Requests require an API Token and Secret Token for authentication..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv add "dlt[hub]" and start loading 2smart cloud data in under 10 minutes.

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What data can I load from 2smart cloud?

Here are some of the endpoints you can load from 2smart cloud:

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How do I authenticate with the 2smart cloud API?

Authentication uses an API Token together with a Secret Token generated in the 2Smart Cloud account settings. The tokens must be included with each request, though the exact header name is not specified.

1. Get your credentials

1. Open a web browser and navigate to https://cloud.2smart.com.
2. Log in with your 2Smart Cloud account credentials.
3. Click on "Account settings" (or go directly to https://cloud.2smart.com/account-settings).
4. Press the "Show all tokens" button.
5. Click the "Generate token" button.
6. Copy the displayed API Token and Secret Token values immediately; the Secret Token is shown only once.
7. Store the tokens securely for use in API calls or SDK configuration.

2. Add them to .dlt/secrets.toml

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[sources._2smart_cloud_source]
api_token = "your_api_token_here"
secret_token = "your_secret_token_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv init
uv add "dlt[hub]"

1. Install the dlt AI Workbench:

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uv run dlthub ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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uv run dlthub ai toolkit install rest-api-pipeline

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the 2smart cloud API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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uv run python _2smart_cloud_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline _2smart_cloud_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset _2smart_cloud_data
The duckdb destination used duckdb:/_2smart_cloud.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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uv run dlthub show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads from the 2smart cloud API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def _2smart_cloud_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://cloud.2smart.com",
            "auth": {
                "type": "api_key",
                "api_key": api_token,
            },
        },
        "resources": [
            
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="_2smart_cloud_pipeline",
        destination="duckdb",
        dataset_name="_2smart_cloud_data",
    )
    load_info = pipeline.run(_2smart_cloud_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("_2smart_cloud_pipeline").dataset()
sessions_df = data..df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM _2smart_cloud_data. LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("_2smart_cloud_pipeline").dataset()
data..df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load 2smart cloud data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication Errors

• Invalid or missing token – If the API Token or Secret Token is omitted or incorrect, the request will be rejected. Ensure both tokens are included exactly as generated.
• Secret Token visibility – The Secret Token is displayed only once; if it is lost you must generate a new token.

Missing Documentation

• No REST endpoints – The platform does not expose a public REST API in the available documentation. Attempts to call unknown endpoints will result in 404 responses.

General Guidance

• Check URL – Verify that you are targeting the correct host (https://cloud.2smart.com) and that the endpoint path is correct if you discover hidden endpoints.
• Rate limits – No rate‑limit information is published; monitor responses for HTTP 429 status codes and implement exponential back‑off if encountered.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-runtime

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack